


'''simple docstring'''
import warnings
from typing import Any, Dict, List, Optional, Union

import numpy as np

from ...audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging


lowerCAmelCase   : int             =				logging.get_logger(__name__)




class        _UpperCamelCase	(    a_):


				'''simple docstring'''


				_snake_case						       =				['''input_values''', '''attention_mask''']

				def __init__(     self							,      a_ = 1							,      a_ = 1_6_0_0_0							,      a_ = 0.0							,      a_ = False							,      a_ = 8_0							,      a_ = 1_6							,      a_ = 6_4							,      a_ = "hann_window"							,      a_ = 1.0							,      a_ = 8_0							,      a_ = 7_6_0_0							,      a_ = 1e-1_0							,      a_ = 2							,      a_ = True							,      **a_							,      )    ->  List[Any]:
					super().__init__(feature_size=_lowercase							,      sampling_rate=_lowercase							,      padding_value=_lowercase							,      **_lowercase							)
					lowercase						:		Optional[Any]   					=		do_normalize
					lowercase						:		Tuple   					=		return_attention_mask

					lowercase						:		Dict   					=		num_mel_bins
					lowercase						:		Union[str, Any]   					=		hop_length
					lowercase						:		Optional[Any]   					=		win_length
					lowercase						:		List[Any]   					=		win_function
					lowercase						:		Dict   					=		frame_signal_scale
					lowercase						:		List[str]   					=		fmin
					lowercase						:		Any   					=		fmax
					lowercase						:		Tuple   					=		mel_floor
					lowercase						:		Union[str, Any]   					=		reduction_factor

					lowercase						:		List[Any]   					=		win_length * sampling_rate // 1_0_0_0
					lowercase						:		Any   					=		hop_length * sampling_rate // 1_0_0_0
					lowercase						:		List[str]   					=		optimal_fft_length(self.sample_size							)
					lowercase						:		Dict   					=		(self.n_fft // 2) + 1

					lowercase						:		int   					=		window_function(window_length=self.sample_size							,      name=self.win_function							,      periodic=_lowercase							)

					lowercase						:		Any   					=		mel_filter_bank(
					    num_frequency_bins=self.n_freqs							,      num_mel_filters=self.num_mel_bins							,      min_frequency=self.fmin							,      max_frequency=self.fmax							,      sampling_rate=self.sampling_rate							,      norm="slaney"							,      mel_scale="slaney"							,      )

					if frame_signal_scale != 1.0:
						warnings.warn(
						    "The argument `frame_signal_scale` is deprecated and will be removed in version 4.30.0 of Transformers"							,      _lowercase							,      )
					if reduction_factor != 2.0:
						warnings.warn(
						    "The argument `reduction_factor` is deprecated and will be removed in version 4.30.0 of Transformers"							,      _lowercase							,      )

				@staticmethod
				# Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
				def 	a__		(     a_							,      a_							,      a_ = 0.0							)    ->  List[np.ndarray]:
					if attention_mask is not None:
						lowercase						:		str   					=		np.array(_lowercase							,      np.intaa							)
						lowercase						:		Optional[Any]   					=		[]

						for vector, length in zip(_lowercase							,      attention_mask.sum(-1							)							):
							lowercase						:		Any   					=		(vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1e-7							)
							if length < normed_slice.shape[0]:
								lowercase						:		List[Any]   					=		padding_value

							normed_input_values.append(_lowercase							)
					else:
						lowercase						:		str   					=		[(x - x.mean()) / np.sqrt(x.var() + 1e-7							) for x in input_values]

					return normed_input_values

				def 	a__		(     self							,      a_							,      )    ->  np.ndarray:
					lowercase						:		int   					=		spectrogram(
					    _lowercase							,      window=self.window							,      frame_length=self.sample_size							,      hop_length=self.sample_stride							,      fft_length=self.n_fft							,      mel_filters=self.mel_filters							,      mel_floor=self.mel_floor							,      log_mel="log10"							,      )
					return log_mel_spec.T

				def __call__(     self							,      a_ = None							,      a_ = None							,      a_ = False							,      a_ = None							,      a_ = False							,      a_ = None							,      a_ = None							,      a_ = None							,      a_ = None							,      **a_							,      )    ->  BatchFeature:
					if audio is None and audio_target is None:
						raise ValueError("You must provide either `audio` or `audio_target` values."							)

					if sampling_rate is not None:
						if sampling_rate != self.sampling_rate:
							raise ValueError(
							    F'''The model corresponding to this feature extractor: {self} was trained using a sampling rate of'''
							    F''' {self.sampling_rate}. Please make sure that the provided audio input was sampled with'''
							    F''' {self.sampling_rate} and not {sampling_rate}.'''							)
					else:
						logger.warning(
						    "It is strongly recommended to pass the ``sampling_rate`` argument to this function. "
						    "Failing to do so can result in silent errors that might be hard to debug."							)

					if audio is not None:
						lowercase						:		Dict   					=		self._process_audio(
						    _lowercase							,      _lowercase							,      _lowercase							,      _lowercase							,      _lowercase							,      _lowercase							,      _lowercase							,      _lowercase							,      **_lowercase							,      )
					else:
						lowercase						:		int   					=		None

					if audio_target is not None:
						lowercase						:		Optional[int]   					=		self._process_audio(
						    _lowercase							,      _lowercase							,      _lowercase							,      _lowercase							,      _lowercase							,      _lowercase							,      _lowercase							,      _lowercase							,      **_lowercase							,      )

						if inputs is None:
							return inputs_target
						else:
							lowercase						:		Optional[Any]   					=		inputs_target["""input_values"""]
							lowercase						:		Union[str, Any]   					=		inputs_target.get("attention_mask"							)
							if decoder_attention_mask is not None:
								lowercase						:		List[Any]   					=		decoder_attention_mask

					return inputs

				def 	a__		(     self							,      a_							,      a_ = False							,      a_ = False							,      a_ = None							,      a_ = False							,      a_ = None							,      a_ = None							,      a_ = None							,      **a_							,      )    ->  BatchFeature:
					lowercase						:		int   					=		isinstance(_lowercase							,      np.ndarray							) and len(speech.shape							) > 1
					if is_batched_numpy and len(speech.shape							) > 2:
						raise ValueError(F'''Only mono-channel audio is supported for input to {self}'''							)
					lowercase						:		Optional[int]   					=		is_batched_numpy or (
					    isinstance(_lowercase							,      (list, tuple)							) and (isinstance(speech[0]							,      (np.ndarray, tuple, list)							))
					)

					if is_batched:
						lowercase						:		str   					=		[np.asarray(_lowercase							,      dtype=np.floataa							) for speech in speech]
					elif not is_batched and not isinstance(_lowercase							,      np.ndarray							):
						lowercase						:		Tuple   					=		np.asarray(_lowercase							,      dtype=np.floataa							)
					elif isinstance(_lowercase							,      np.ndarray							) and speech.dtype is np.dtype(np.floataa							):
						lowercase						:		Tuple   					=		speech.astype(np.floataa							)

					# always return batch
					if not is_batched:
						lowercase						:		int   					=		[speech]

					# needed to make pad() work on spectrogram inputs
					lowercase						:		Optional[int]   					=		self.feature_size

					# convert into correct format for padding
					if is_target:
						lowercase						:		Any   					=		[self._extract_mel_features(_lowercase							) for waveform in speech]
						lowercase						:		List[Any]   					=		BatchFeature({"input_values": features}							)
						lowercase						:		List[Any]   					=		self.num_mel_bins
					else:
						lowercase						:		List[Any]   					=		BatchFeature({"input_values": speech}							)

					lowercase						:		Any   					=		self.pad(
					    _lowercase							,      padding=_lowercase							,      max_length=_lowercase							,      truncation=_lowercase							,      pad_to_multiple_of=_lowercase							,      return_attention_mask=_lowercase							,      **_lowercase							,      )

					lowercase						:		List[Any]   					=		feature_size_hack

					# convert input values to correct format
					lowercase						:		Union[str, Any]   					=		padded_inputs["""input_values"""]
					if not isinstance(input_values[0]							,      np.ndarray							):
						lowercase						:		str   					=		[np.asarray(_lowercase							,      dtype=np.floataa							) for array in input_values]
					elif (
					    not isinstance(_lowercase							,      np.ndarray							)
					    and isinstance(input_values[0]							,      np.ndarray							)
					    and input_values[0].dtype is np.dtype(np.floataa							)
					):
						lowercase						:		Optional[int]   					=		[array.astype(np.floataa							) for array in input_values]
					elif isinstance(_lowercase							,      np.ndarray							) and input_values.dtype is np.dtype(np.floataa							):
						lowercase						:		Tuple   					=		input_values.astype(np.floataa							)

					# convert attention_mask to correct format
					lowercase						:		Optional[Any]   					=		padded_inputs.get("attention_mask"							)
					if attention_mask is not None:
						lowercase						:		List[str]   					=		[np.asarray(_lowercase							,      dtype=np.intaa							) for array in attention_mask]

					# zero-mean and unit-variance normalization
					if not is_target and self.do_normalize:
						lowercase						:		Tuple   					=		(
						    attention_mask
						    if self._get_padding_strategies(_lowercase							,      max_length=_lowercase							) is not PaddingStrategy.DO_NOT_PAD
						    else None
						)
						lowercase						:		Dict   					=		self.zero_mean_unit_var_norm(
						    padded_inputs["input_values"]							,      attention_mask=_lowercase							,      padding_value=self.padding_value							)

					if return_tensors is not None:
						lowercase						:		Union[str, Any]   					=		padded_inputs.convert_to_tensors(_lowercase							)

					return padded_inputs


				def 	a__		(     self							)    ->  Dict[str, Any]:
					lowercase						:		Tuple   					=		super().to_dict()

					# Don't serialize these as they are derived from the other properties.
					lowercase						:		List[Any]   					=		["""window""", """mel_filters""", """sample_size""", """sample_stride""", """n_fft""", """n_freqs"""]
					for name in names:
						if name in output:
							del output[name]

					return output





from __future__ import annotations

# This is the precision for this function which can be altered.
# It is recommended for users to keep this number greater than or equal to 10.
__snake_case				:      Optional[Any]   		=			10


def 					_UpperCAmelCase				(						a__  ,	a__  ,	a__  ,	a__):

			'''simple docstring'''



			for i in range(a__  ,	a__):
						if array[i] == target:
									return i
			return -1


def 					_UpperCAmelCase				(						a__  ,	a__):

			'''simple docstring'''



			a_						:						Any 							= 0
			a_						:						List[str] 							= len(a__)
			while left <= right:
						if right - left < precision:
									return lin_search(a__  ,	a__  ,	a__  ,	a__)

						a_						:						Dict 							= (left + right) // 3 + 1
						a_						:						Optional[Any] 							= 2 * (left + right) // 3 + 1

						if array[one_third] == target:
									return one_third
						elif array[two_third] == target:
									return two_third

						elif target < array[one_third]:
									a_						:						int 							= one_third - 1
						elif array[two_third] < target:
									a_						:						Any 							= two_third + 1

						else:
									a_						:						Optional[int] 							= one_third + 1
									a_						:						int 							= two_third - 1
			else:
						return -1


def 					_UpperCAmelCase				(						a__  ,	a__  ,	a__  ,	a__):

			'''simple docstring'''



			if left < right:
						if right - left < precision:
									return lin_search(a__  ,	a__  ,	a__  ,	a__)
						a_						:						Optional[int] 							= (left + right) // 3 + 1
						a_						:						Union[str, Any] 							= 2 * (left + right) // 3 + 1

						if array[one_third] == target:
									return one_third
						elif array[two_third] == target:
									return two_third

						elif target < array[one_third]:
									return rec_ternary_search(a__  ,	one_third - 1  ,	a__  ,	a__)
						elif array[two_third] < target:
									return rec_ternary_search(two_third + 1  ,	a__  ,	a__  ,	a__)
						else:
									return rec_ternary_search(one_third + 1  ,	two_third - 1  ,	a__  ,	a__)
			else:
						return -1


if __name__ == "__main__":
	import doctest

	doctest.testmod()

	__snake_case				:      Optional[int]   		=			input("""Enter numbers separated by comma:\n""").strip()
	__snake_case				:      Optional[int]   		=			[int(item.strip()) for item in user_input.split(""",""")]
	assert collection == sorted(collection), F"List must be ordered.\n{collection}."
	__snake_case				:      int   		=			int(input("""Enter the number to be found in the list:\n""").strip())
	__snake_case				:      List[Any]   		=			ite_ternary_search(collection, target)
	__snake_case				:      Optional[int]   		=			rec_ternary_search(0, len(collection) - 1, collection, target)
	if resulta != -1:
		print(F"""Iterative search: {target} found at positions: {resulta}""")
		print(F"""Recursive search: {target} found at positions: {resulta}""")
	else:
		print("""Not found""")


